Hello,
I am running PROC MIXED on an outcome variable called Reaction_Time and the predictors Day, Day*Day (quadratic trajectory), Sleepiness, Day*Sleepiness, Day*Day*Sleepiness, and Time. See the code below for my analysis:
Proc Mixed data=SR.Cog NOCLPRINT NOITPRINT COVTEST METHOD=ML;
CLASS Participant;
MODEL Reaction_Time = Day Day*Day Sleepiness Day*Sleepiness Day*Day*Sleepiness Time / SOLUTION;
RANDOM intercept / TYPE=AR(1) SUBJECT=Participant; run;
When using such code I have no issues with the SAS output. However, if I substitute Response_Speed, the inversely transformed variable of Reaction_Time (that is, 1/Reaction_Time), in place of Reaction_Time, the "Solution for Fixed Effects" produces standard errors of 0 for my effects for "Day*Day*Sleepiness" and "Time." See the attached screenshot of my output:
The "t Values" are consequently infinity, and the p-values are < .0001. As you can see, in "Type 3 Tests of Fixed Effects," this phenomenon does not seem to be occurring; the p-values are over .0001. I tried searching online for standard errors of 0; it was mentioned that the model may be overparameterized. I'm not sure why this would be the case, as the model ran just fine with the non-transformed variable (Reaction_Time). Can someone clarify and help me fix this error?
Thanks!
confooseddesi89
I'm guessing your scaling of variables is very different and the zero happens because of arithmetic underflow.
I'm guessing your scaling of variables is very different and the zero happens because of arithmetic underflow.
That was it! Changed the scaling of my variable (multiplied by 1000) and I didn't get 0 standard errors anymore. Thanks!
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